2007
DOI: 10.1029/2006wr005780
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Efficient extraction of networks from three‐dimensional porous media

Abstract: [1] Fluid flow through porous media, and the thermal, electrical, and acoustic properties of these materials, is largely controlled by the geometry and topology (GT) of the pore system, which can be considered as a network. Network extraction techniques have been applied in many research fields, including shape representation, pattern recognition, and artificial intelligence. However, the set of algorithms presented here significantly improves the efficiency of common thinning algorithms by introducing a suffi… Show more

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Cited by 151 publications
(104 citation statements)
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“…These models, however, are based upon simplified representations of the complex pore geometry [17], which restricts their predictive capability and accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…These models, however, are based upon simplified representations of the complex pore geometry [17], which restricts their predictive capability and accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, the individual particle behavior is not accurately reproduced and this limits the application of the model to problems such as strain localization, segregating phenomena, effects of local heterogeneities in porosity and internal erosion by transport of fines, which are all inherently heterogeneous on the microscale; -Pore-network modeling has been commonly developed to predict the permeability of materials but has also been extended to multiphase flow (Bryant and Blunt, 1992). It relies on the representation of the pore space as a network (Jiang et al, 2007). Crucial for its success is an adequate definition of how fluids are exchanged between pores with respect to the local pore geometry.…”
Section: Introductionmentioning
confidence: 99%
“…The basic principle of the AB algorithm is the constraining of the maximal balls on the centrally located medial axis. The algorithm combines the advantages of the media axis (Lindquist et al 1996;Jiang et al 2007) and maximal ball (Dong and Blunt 2009) approaches and consists of six steps: (1) building the inscribed spheres; (2) identifying the medial axis; (3) constraining the maximal balls (MBs) on the medial axis; (4) defining the pores and pore throats; (5) segmenting the pores and pore throats; and (6) calculating the parameters of the pores and pore throats. The flow chart is shown in Fig.…”
Section: Pore Network Extraction: Ab Algorithmmentioning
confidence: 99%